versions/1.0.0/_sources/api/python/image/image.txt

# Image API

## Overview
This document summarizes supporting functions and iterators to read and process
images provided in
```eval_rst
.. autosummary::
    :nosignatures:

    mxnet.image
```

## Image processing functions
```eval_rst
.. currentmodule:: mxnet
.. autosummary::
    :nosignatures:

    image.imdecode
    image.scale_down
    image.resize_short
    image.fixed_crop
    image.random_crop
    image.center_crop
    image.color_normalize
    image.random_size_crop
```    

## Image iterators
Iterators support loading image from binary `Record IO` and raw image files.
```eval_rst
.. autosummary::
    :nosignatures:

    image.ImageIter
```
```python
>>> data_iter = mx.image.ImageIter(batch_size=4, data_shape=(3, 224, 224), label_width=1,
                                   path_imglist='data/custom.lst')
>>> data_iter.reset()
>>> for data in data_iter:
...     d = data.data[0]
...     print(d.shape)
>>> # we can apply lots of augmentations as well
>>> data_iter = mx.image.ImageIter(4, (3, 224, 224), path_imglist='data/custom.lst',
                                   rand_crop=resize=True, rand_mirror=True, mean=True,
                                   brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1,
                                   pca_noise=0.1, rand_gray=0.05)
>>> data = data_iter.next()
>>> # specify augmenters manually is also supported
>>> data_iter = mx.image.ImageIter(32, (3, 224, 224), path_rec='data/caltech.rec',
                                   path_imgidx='data/caltech.idx', shuffle=True,
                                   aug_list=[mx.image.HorizontalFlipAug(0.5),
                                   mx.image.ColorJitterAug(0.1, 0.1, 0.1)])
```

We use helper function to initialize augmenters
```eval_rst
    .. currentmodule:: mxnet
.. autosummary::
    :nosignatures:

    image.CreateAugmenter
```

A list of supporting augmenters
```eval_rst
.. autosummary::
    :nosignatures:

    image.Augmenter
    image.SequentialAug
    image.RandomOrderAug
    image.ResizeAug
    image.ForceResizeAug
    image.RandomCropAug
    image.RandomSizedCropAug
    image.CenterCropAug
    image.BrightnessJitterAug
    image.ContrastJitterAug
    image.SaturationJitterAug
    image.HueJitterAug
    image.ColorJitterAug
    image.LightingAug
    image.ColorNormalizeAug
    image.RandomGrayAug
    image.HorizontalFlipAug
    image.CastAug
```

Similar to `ImageIter`, `ImageDetIter` is designed for `Object Detection` tasks.
```eval_rst
.. autosummary::
    :nosignatures:

    image.ImageDetIter
```

```python
>>> data_iter = mx.image.ImageDetIter(batch_size=4, data_shape=(3, 224, 224),
                                      path_imglist='data/train.lst')
>>> data_iter.reset()
>>> for data in data_iter:
...     d = data.data[0]
...     l = data.label[0]
...     print(d.shape)
...     print(l.shape)
```

Unlike object classification with fixed label_width, object count may vary from
image to image. Thus we have special format for object detection labels.
Usually the `lst` file generated by `tools/im2rec.py` is a list of
```
index_0  label_0  image_path_0
index_1  label_1  image_path_1
```
Where `label_N` is a number a of fixed-width vector.
The format of label used in object detection is a variable length vector
```
A  B  [extra header]  [(object0), (object1), ... (objectN)]
```
Where A is the width of header (2 + length of extra header), B is the width of each object.
Extra header is optional and used for inserting helper information such as (width, height).
Each object is usually 5 or 6 numbers describing the object properties, for example:
[id, xmin, ymin, xmax, ymax, difficulty]
Putting all together, we have a `lst` file for object detection:
```
0  4  5  640  480  1  0.1  0.2  0.8  0.9  2  0.5  0.3  0.6  0.8  data/xxx.jpg
1  4  5  480  640  3  0.05  0.16  0.75  0.9  data/yyy.jpg
2  4  5  500  600  2  0.6  0.1  0.7  0.5  0  0.1  0.3  0.2  0.4  3  0.25  0.25  0.3  0.3 data/zzz.jpg
...
```

A helper function to initialize Augmenters for `Object detection` task
```eval_rst
.. autosummary::
    :nosignatures:

    image.CreateDetAugmenter
  ```

Since `Detection` task is sensitive to object localization, any modification
to image that introduced localization shift will require correction to label,
and a list of augmenters specific for `Object detection` is provided
```eval_rst
.. autosummary::
    :nosignatures:

    image.DetBorrowAug
    image.DetRandomSelectAug
    image.DetHorizontalFlipAug
    image.DetRandomCropAug
    image.DetRandomPadAug
```

## API Reference

<script type="text/javascript" src='../../_static/js/auto_module_index.js'></script>

```eval_rst
.. automodule:: mxnet.image
.. autoclass:: mxnet.image.ImageIter
    :members:

.. automethod:: mxnet.image.imdecode
.. automethod:: mxnet.image.scale_down
.. automethod:: mxnet.image.resize_short
.. automethod:: mxnet.image.fixed_crop
.. automethod:: mxnet.image.random_crop
.. automethod:: mxnet.image.center_crop
.. automethod:: mxnet.image.color_normalize
.. automethod:: mxnet.image.random_size_crop

.. autoclass:: mxnet.image.Augmenter
    :members:
.. autoclass:: mxnet.image.ResizeAug
.. autoclass:: mxnet.image.ForceResizeAug
.. autoclass:: mxnet.image.RandomCropAug
.. autoclass:: mxnet.image.RandomSizedCropAug
.. autoclass:: mxnet.image.CenterCropAug
.. autoclass:: mxnet.image.RandomOrderAug
.. autoclass:: mxnet.image.BrightnessJitterAug
.. autoclass:: mxnet.image.ContrastJitterAug
.. autoclass:: mxnet.image.SaturationJitterAug
.. autoclass:: mxnet.image.HueJitterAug
.. autoclass:: mxnet.image.ColorJitterAug
.. autoclass:: mxnet.image.LightingAug
.. autoclass:: mxnet.image.ColorNormalizeAug
.. autoclass:: mxnet.image.RandomGrayAug
.. autoclass:: mxnet.image.HorizontalFlipAug
.. autoclass:: mxnet.image.CastAug

.. automethod:: mxnet.image.CreateAugmenter

.. autoclass:: mxnet.image.ImageDetIter
    :members:
.. autoclass:: mxnet.image.DetAugmenter
    :members:
.. autoclass:: mxnet.image.DetBorrowAug
.. autoclass:: mxnet.image.DetRandomSelectAug
.. autoclass:: mxnet.image.DetHorizontalFlipAug
.. autoclass:: mxnet.image.DetRandomCropAug
.. autoclass:: mxnet.image.DetRandomPadAug

.. automethod:: mxnet.image.CreateDetAugmenter
```
<script>auto_index("api-reference");</script>